Why does LLM reasoning feel so incredibly inconsistent lately

I’ve been staring at my terminal for the last three hours trying to figure out if I’m actually losing my mind or if the current state of model reasoning is just fundamentally broken. We talk a lot about prompt engineering and building robust AI workflows, but nobody seems to want to talk about the sheer randomness of how these models handle logic when you move from a controlled environment to a real-world deployment.

I was working on a small automated agent designed to parse complex, unstructured JSON data from various API endpoints. Everything was working perfectly during my initial testing phase. I had my system prompts dialed in, my few-shot examples were solid, and the model was following the schema like a professional. Then, I pushed it to a live data stream, and everything went sideways.

The specific issue wasn't a crash or a syntax error—those are easy to fix. It was a logical hallucination that was so subtle I almost missed it. The model started confidently asserting that a value was null when the key actually existed with an empty string, and then, in the very next turn, it would flip-flop and claim the key was missing entirely.

Here is a snippet of the logic loop I kept hitting:

{
  "error": "Logic Drift Detected",
  "input_context": "user_metadata_v4",
  "model_output_sequence": [
    {"step": 1, "observation": "Key 'subscription_status' found: 'active'"},
    {"step": 2, "observation": "Key 'subscription_status' is missing from payload"},
    {"step": 3, "observation": "Key 'subscription_status' is null"}
  ],
  "diagnostic": "Non-deterministic reasoning path in high-entropy input"
}

When I tried to debug this using a standard step-by-step prompting approach—basically forcing the model to "think out loud" before giving the final answer—the problem didn't go away; it just became more visible. It’s like the model is trying to please the user by finding a pattern that isn't actually there, rather than strictly adhering to the provided context.

I’ve tried several different tactics to stabilize this:

• Temperature adjustment: Dropping the temperature to 0.0 usually helps, but even then, the internal "attention" seems to drift when the input context gets too long.
• Strict Schema Enforcement: Using JSON mode or function calling, which should theoretically prevent this, but the model still makes the logical leap that the data is absent when it's just formatted strangely.
• Chain-of-Thought (CoT) refinement: I tried to build a more complex prompt engineering strategy where the model verifies its own findings, but it often ends up "hallucinating the verification" too.

Is anyone else seeing this kind of degradation in reasoning stability? It feels like as we push for more complex LLM agents that can handle multi-step tasks, we are hitting a ceiling where the probabilistic nature of these models creates a "drift" that simple prompting can't fix. I'm wondering if we need to move away from relying on a single massive model and instead start looking at multi-agent architectures where one model acts as a strict validator for another.

If you're building anything mission-critical, be prepared for the fact that "it worked in my test script" means absolutely nothing once you hit actual production data.